The Four Types of Data Analytics
To get great insights from your data, you need to familiarize yourself with the four types of data analytics to understand where the business’s current position is and where there is potential to grow.
Briefly they are:
- Descriptive analytics
Allows you to pull trends from raw data and succinctly describe past or present events. Descriptive analytics answers the question, “What happened?” Data visualization is a natural fit for communicating this type of analysis - Diagnostic analytics
Addresses the next logical question, “Why did this happen?” In this step, analysts will investigate by comparing coexisting trends, unknown correlations between variables and determining causal relationships if possible. - Predictive analytics
As the term literally suggests, we want to predict future trends or events that answer the question “What might happen?” - Prescriptive analytics
We want to answer the question of “what should we do next?” with this type of analysis. Considering all possible scenarios and factors, this analysis suggests possible and more efficient courses of action.
The power of predictive analytics is its ability to predict outcomes and trends before they happen. Using historical data and information about the current situation, it can predict future outcomes. Identifying the best predictive analytics model for your business is a crucial part of business strategy. The use of these models has become so popular and gives so much benefit to businesses that, for example, nowadays analytical tools used to build dashboards have already incorporated them just at one click distance to be applied. In the following post, we will introduce you to the fundamental concept of predictive analytics and you will discover all its potential.
This post is the closing topic of our Analytics Beyond Dashboards series. Please take a moment to explore our previous posts in the series if you have not yet:
- How to Define KPIs for impactful insights
- Mobile App Analytics vs. Website Analytics
- Marketing Analytics: Improving the success of marketing campaigns
- Product Analytics: Cohort and retention analyses
How Do Predictive Analytics Models Work?
All predictive models are trained using one or more predictive algorithms. It is a cyclic process that begins with the understanding of the business objective and the data, followed by data preparation. This step is essential, as predictive models will only be able to predict based on the information that we provide to them.
Now, with a solid data foundation, the model is trained, and results are analyzed. The analysis of the results not only involves looking into the final number that a forecast can give us or the accuracy of the predictive algorithm. As analysts, we also investigate the details; we identify the variables that contribute to the result, as might be key for maintenance.
The last step is to deploy the model, ensuring it seamlessly integrates into the intended application and begins making an impact.
A predictive model is not a one-time task; the model needs to be validated every certain time, might need to be retrained, for example, because of new data inputs or adjustments in the predictive algorithm for a better result.
The Top 5 Predictive Analysis Models
1. Classification models
These models arrange the data in categories based on what they learn from the historical data. Classification models can provide a binary solution to facilitate a comprehensive analysis.
Some examples of classification algorithms are: Logistic regression, Decision trees, Random Forest, Neural networks, Naïve Bayes.
2. Clustering models
Clustering models help sort data into distinct groups based on multiple attributes. This analytic model class is the best choice, for example, for dividing the data into smaller data sets with common characteristics for effective marketing strategies. You can further divide predictive clustering modelling into two categories: hard clustering and soft clustering. Hard clustering helps to analyze whether the data point belongs to the data cluster or not. However, soft clustering helps to assign a probability to the data point when joining the data cluster.
Some popular algorithms are K-Means, Mean-Shift, Density-Based Spatial Clustering with Noise (DBSCAN).
3. Forecasting models
This class of predictive analytics models helps businesses estimate the numeric value of new data based on historical data. The most important advantage of forecasting is that it also considers multiple input parameters simultaneously. This is why the forecast model is one of the most commonly used predictive analytics models in businesses.
4. Outlier models
Unlike the classification and forecast models, which work on normal historical data, the outlier model of predictive analytics considers the anomalous data entries from the given dataset for predicting future outcomes. The model can analyze the unusual data either by itself or by combining it with other categories and numbers present. Examples of applications can be found in the financial or retail industries were identifying outliers (ex. suspicions money movements in bank transactions) can save a lot of money by identifying fraud.
We can say that Isolation Forest, Minimum Covariance Determinant (MCD) and Local Outlier Factor (LOF) are the most popular.
5,. Time series models
As the name says, the best applicability is when time is the input parameter for our prediction. This predictive model works with data points drawn from the historical data to develop a numerical metric and predict future trends. This model involves the conventional method of finding the process and dependencies of various business variables. Also, it considers the extraneous factors and risks that can affect the business on a large scale over time.
A few of the common time series models are: ARIMA and Prophet.
Predictive Analytics Tools
The range of possibilities is wide, from no-code tools to machine learning algorithms. It is on each business to decide what best fits their needs, expertise, time schedule for application, etc. Some tools are complete workspaces while others can be integrated with existing tools. There are solutions for cloud deployments and on-prem.
The major shift in modern predictive analytics tools is that they have become easier to implement compared to existing models or building new ones from scratch. The new capabilities of automated machine learning reduce the need to deeply understand how the variables affect each other, automatically choosing the best combination of algorithms for a given task and reducing the time that an analyst needs to spend writing code.
Best practices to select the right predictive analytics tools for your case
Here we will list some considerations as best practices to select the most appropriate predictive analytic tool for your case:
- Think first about the company’s application needs and then search for the tool, keeping in mind that an individual tool or a group of services could be chosen. Not all businesses need the full package, and it could be the case that existing tools for business intelligence, analytics or CRM already support your needs.
- Consider the use of automated machine learning services or prebuilt models, templates, or toolkits in conjunction with standard languages, like R or Python, and visualization services, like Tableau or Qlik Sense, to add unique attributes to your solutions.
- Think about who will be using these tools. Some users might look for tools to augment data discovery, data preparation and model development, while others might look for services that provide guardrails for common business requirements and support collaborative development across teams.
- Plan on regular enhancements of applications based on changes in the data, improving accuracy and performance, to make applications more user-friendly and intuitive, overcome security threats and reduce costs with better efficiency.
The Data Analyst Role
Predictive Analytics is also known as predictive modelling, with the first term being the most preferred one for commercial applications and the second one in an academic environment. Successful use of predictive analytics depends heavily on unfettered access to sufficient volumes of accurate, clean, and relevant data.
Predictive models for everyday business cases can run and give accurate results in real time. To align with this trend, who is better than a data analyst as the most indicated and efficient person to be able to make a quick and accurate choice of a model and the evaluation of its results? The reason is based on the fact that this person is already familiar with the data due to the data profiling and data preparation made for populating the model, and of course knows the business case since it needs to be given as input for the previous task.
Evaluating the performance of predictive models is a critical task that data analysts can easily assess using a variety of metrics. Also, their skills in data visualization allow them to present the interpretation of the model’s results in the most user-friendly way possible.
Conclusion
Predictive analytics is a wide and important part of data analytics. Exploring its endless possibilities can empower your business with data-driven insights for a better decision-making strategy.
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